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minimal value for n is 3, returning requested palette with 3 different levels
minimal value for n is 3, returning requested palette with 3 different levels
minimal value for n is 3, returning requested palette with 3 different levels
minimal value for n is 3, returning requested palette with 3 different levels
Check with dataset as a part of the model. (They should not be, as they are generated by the same code, but different seeds.)
Call:
lm(formula = log(CommunitySize) ~ log(Basals) + log(Consumers) +
Dataset, data = plotScalingData)
Residuals:
Min 1Q Median 3Q Max
-0.83712 -0.17860 0.01358 0.20886 0.78899
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 0.91641 0.11947 7.670 5.05e-11 ***
log(Basals) 0.23818 0.02014 11.825 < 2e-16 ***
log(Consumers) 0.03652 0.01856 1.968 0.0528 .
Dataset2021-05 0.07433 0.07197 1.033 0.3050
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 0.3089 on 75 degrees of freedom
Multiple R-squared: 0.677, Adjusted R-squared: 0.664
F-statistic: 52.39 on 3 and 75 DF, p-value: < 2.2e-16
Datasets do not look to have a statistically significant effect. Without comparing the models (would need to do in any publication), the model without the dataset effect is:
Call:
lm(formula = log(CommunitySize) ~ log(Basals) + log(Consumers),
data = plotScalingData)
Residuals:
Min 1Q Median 3Q Max
-0.86732 -0.17301 -0.02186 0.20156 0.74221
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 0.93258 0.11850 7.870 1.95e-11 ***
log(Basals) 0.24312 0.01957 12.421 < 2e-16 ***
log(Consumers) 0.03800 0.01851 2.053 0.0435 *
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 0.309 on 76 degrees of freedom
Multiple R-squared: 0.6724, Adjusted R-squared: 0.6638
F-statistic: 77.99 on 2 and 76 DF, p-value: < 2.2e-16
Adding this to the plot:
minimal value for n is 3, returning requested palette with 3 different levels
minimal value for n is 3, returning requested palette with 3 different levels
minimal value for n is 3, returning requested palette with 3 different levels
minimal value for n is 3, returning requested palette with 3 different levels
Not particularly convincing, but it does agree with general ideas (consumers less important than basals).
Call:
lm(formula = log(CommunitySize) ~ log(Basals) + log(Consumers) -
1, data = plotScalingData)
Residuals:
Min 1Q Median 3Q Max
-0.80814 -0.13633 0.05056 0.40151 1.03117
Coefficients:
Estimate Std. Error t value Pr(>|t|)
log(Basals) 0.33969 0.02041 16.642 < 2e-16 ***
log(Consumers) 0.14852 0.01614 9.202 4.89e-14 ***
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 0.4136 on 77 degrees of freedom
Multiple R-squared: 0.9598, Adjusted R-squared: 0.9587
F-statistic: 918.9 on 2 and 77 DF, p-value: < 2.2e-16
minimal value for n is 3, returning requested palette with 3 different levels
minimal value for n is 3, returning requested palette with 3 different levels
minimal value for n is 3, returning requested palette with 3 different levels
minimal value for n is 3, returning requested palette with 3 different levels
Call:
lm(formula = CommunitySize ~ log(Basals) + log(Consumers), data = plotScalingData)
Residuals:
Min 1Q Median 3Q Max
-4.867 -1.628 -0.500 1.427 9.556
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 0.09911 0.98519 0.101 0.9201
log(Basals) 1.93666 0.16273 11.901 <2e-16 ***
log(Consumers) 0.26912 0.15390 1.749 0.0844 .
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 2.569 on 76 degrees of freedom
Multiple R-squared: 0.6525, Adjusted R-squared: 0.6433
F-statistic: 71.34 on 2 and 76 DF, p-value: < 2.2e-16
minimal value for n is 3, returning requested palette with 3 different levels
minimal value for n is 3, returning requested palette with 3 different levels
minimal value for n is 3, returning requested palette with 3 different levels
minimal value for n is 3, returning requested palette with 3 different levels